Overview

Dataset statistics

Number of variables12
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

monetary is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
recency is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtde_items is highly overall correlated with monetary and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with monetary and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with monetary and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtde_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.43524464)Skewed
frequency is highly skewed (γ1 = 24.87657216)Skewed
qtdade_itens_retornados is highly skewed (γ1 = 51.78903194)Skewed
avg_basket_size is highly skewed (γ1 = 44.67603937)Skewed
customer_id has unique valuesUnique
recency has 34 (1.1%) zerosZeros
qtdade_itens_retornados has 1480 (49.9%) zerosZeros

Reproduction

Analysis started2023-04-30 02:53:50.130554
Analysis finished2023-04-30 02:54:06.471067
Duration16.34 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.371
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:06.534320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.1403
Coefficient of variation (CV)0.11258013
Kurtosis-1.2061649
Mean15270.371
Median Absolute Deviation (MAD)1489
Skewness0.032199506
Sum45322461
Variance2955443.5
MonotonicityNot monotonic
2023-04-29T23:54:06.647377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
12670 1
 
< 0.1%
17734 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
Other values (2958) 2958
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

monetary
Real number (ℝ)

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.7094
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:06.756963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32308.805
95-th percentile7221.795
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.96

Descriptive statistics

Standard deviation10582.241
Coefficient of variation (CV)3.8484942
Kurtosis353.83989
Mean2749.7094
Median Absolute Deviation (MAD)671.625
Skewness16.775076
Sum8161137.5
Variance1.1198382 × 108
MonotonicityNot monotonic
2023-04-29T23:54:06.852350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.96 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
1353.74 2
 
0.1%
889.93 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
734.94 2
 
0.1%
Other values (2943) 2948
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140438.72 1
< 0.1%
124564.53 1
< 0.1%
117375.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.303908
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:06.966693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.764827
Coefficient of variation (CV)1.2093328
Kurtosis2.7759634
Mean64.303908
Median Absolute Deviation (MAD)26
Skewness1.7978803
Sum190854
Variance6047.3683
MonotonicityNot monotonic
2023-04-29T23:54:07.063936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2218
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7237197
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:07.186808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8580052
Coefficient of variation (CV)1.5475959
Kurtosis190.76806
Mean5.7237197
Median Absolute Deviation (MAD)2
Skewness10.764858
Sum16988
Variance78.464257
MonotonicityNot monotonic
2023-04-29T23:54:07.296746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 786
26.5%
3 497
16.7%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 786
26.5%
3 497
16.7%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_items
Real number (ℝ)

Distinct1665
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.6779
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:07.408898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.35
Q1296
median638
Q31399
95-th percentile4407.6
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5883.956
Coefficient of variation (CV)3.6621877
Kurtosis466.99678
Mean1606.6779
Median Absolute Deviation (MAD)419.5
Skewness17.875434
Sum4768620
Variance34620938
MonotonicityNot monotonic
2023-04-29T23:54:07.522370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
246 8
 
0.3%
516 7
 
0.2%
394 7
 
0.2%
Other values (1655) 2885
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
79963 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
62812 1
< 0.1%
58243 1
< 0.1%
57785 1
< 0.1%

qtde_products
Real number (ℝ)

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.66712
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:07.648394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.87945
Coefficient of variation (CV)2.2000961
Kurtosis354.76843
Mean122.66712
Median Absolute Deviation (MAD)44
Skewness15.705296
Sum364076
Variance72834.917
MonotonicityNot monotonic
2023-04-29T23:54:07.754866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 45
 
1.5%
20 38
 
1.3%
35 35
 
1.2%
29 33
 
1.1%
19 33
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
6 29
 
1.0%
Other values (459) 2629
88.6%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 27
0.9%
10 27
0.9%
ValueCountFrequency (%)
7837 1
< 0.1%
5670 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.915666
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:07.869856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.915888
Q113.120801
median17.977335
Q324.989429
95-th percentile90.498417
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868627

Descriptive statistics

Standard deviation1037.1087
Coefficient of variation (CV)19.976797
Kurtosis2889.7351
Mean51.915666
Median Absolute Deviation (MAD)5.9901484
Skewness53.435245
Sum154085.7
Variance1075594.5
MonotonicityNot monotonic
2023-04-29T23:54:07.972723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.353402
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:08.087089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.927198
median48.267857
Q385.375
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.447802

Descriptive statistics

Standard deviation63.553446
Coefficient of variation (CV)0.94358183
Kurtosis4.8848377
Mean67.353402
Median Absolute Deviation (MAD)26.267857
Skewness2.0624779
Sum199904.9
Variance4039.0405
MonotonicityNot monotonic
2023-04-29T23:54:08.200099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
21 17
 
0.6%
46 17
 
0.6%
11 17
 
0.6%
1 16
 
0.5%
Other values (1248) 2776
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1224
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11382141
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:08.301142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088935048
Q10.016336535
median0.025898352
Q30.049426591
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033090056

Descriptive statistics

Standard deviation0.40822261
Coefficient of variation (CV)3.5865187
Kurtosis989.04899
Mean0.11382141
Median Absolute Deviation (MAD)0.012196886
Skewness24.876572
Sum337.82195
Variance0.1666457
MonotonicityNot monotonic
2023-04-29T23:54:08.394408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.02777777778 17
 
0.6%
0.0625 17
 
0.6%
0.02380952381 16
 
0.5%
0.08333333333 15
 
0.5%
0.09090909091 15
 
0.5%
0.02941176471 14
 
0.5%
0.03448275862 14
 
0.5%
0.02564102564 13
 
0.4%
0.02127659574 13
 
0.4%
Other values (1214) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtdade_itens_retornados
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.177898
Minimum0
Maximum80995
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:08.503090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.65
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.7506
Coefficient of variation (CV)24.329394
Kurtosis2764.598
Mean62.177898
Median Absolute Deviation (MAD)1
Skewness51.789032
Sum184544
Variance2288414.3
MonotonicityNot monotonic
2023-04-29T23:54:08.604914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.3254
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:08.715976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172
Q3281.375
95-th percentile599.58
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.1375

Descriptive statistics

Standard deviation791.63469
Coefficient of variation (CV)3.1751065
Kurtosis2255.5038
Mean249.3254
Median Absolute Deviation (MAD)82.625
Skewness44.676039
Sum739997.79
Variance626685.48
MonotonicityNot monotonic
2023-04-29T23:54:08.814298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
71 7
 
0.2%
Other values (1963) 2881
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct909
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.473826
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-04-29T23:54:08.922479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322
95-th percentile45.9125
Maximum259
Range258.8
Interquartile range (IQR)14.333333

Descriptive statistics

Standard deviation15.452856
Coefficient of variation (CV)0.88434305
Kurtosis29.388198
Mean17.473826
Median Absolute Deviation (MAD)6.6
Skewness3.4401001
Sum51862.315
Variance238.79077
MonotonicityNot monotonic
2023-04-29T23:54:09.043349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 43
 
1.4%
9 42
 
1.4%
16 41
 
1.4%
8 39
 
1.3%
17 37
 
1.2%
14 37
 
1.2%
11 36
 
1.2%
7 36
 
1.2%
15 34
 
1.1%
5 34
 
1.1%
Other values (899) 2589
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-04-29T23:54:05.031993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:51.389273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.585817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.747354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.934803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.096343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:57.721513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.954817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.111234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.173172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.305341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.393085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.115276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:51.556148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.675454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.842130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.023732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.210041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:57.823824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.049542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.202350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.256383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.390215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.479232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.212041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:51.646628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.759321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.932003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.112825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.317657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:57.927619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.139489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.282506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.340955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.479444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.562971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.312268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:51.741794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.850671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.027104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.201829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.422459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.035573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.244386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.368550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.427906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.577860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.666357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.396585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:51.822841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.943763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.118075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.279466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.505564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.130146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.344615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.449689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.515981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.659502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.755965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.495707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:51.920535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.050966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.238430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.376245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.613365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.237492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.448691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.537913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.604029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.752096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.848285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.600136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.019484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.146358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.346193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.472723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.722607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.346040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.545576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.628528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.745511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.852362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:04.429817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.688030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.102362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.247252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.445147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.578910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.817024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.442193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.640680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.716019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.840389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.943968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:04.527287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.786442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.204701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.349170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.539884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.686178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.919388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.552915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.732936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.804056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.941837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.032922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:04.634539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.891716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.296110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.446687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.637318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.794456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:57.401011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.653506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.824645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.892085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.032168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.122480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:04.739254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:05.995880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.394223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.554116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.739809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:55.899114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:57.517192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.754263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:59.921281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.988024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.129879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.217325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:04.839201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:06.091597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:52.499591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:53.652152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:54.844270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:56.008422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:57.631750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:53:58.856030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:00.023923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:01.082933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:02.222247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:03.310518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-29T23:54:04.937181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-04-29T23:54:09.136395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idmonetaryrecencyqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtdade_itens_retornadosavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0760.0010.025-0.0700.012-0.1300.019-0.002-0.063-0.123-0.017
monetary-0.0761.000-0.4150.7700.9270.7440.246-0.2480.0900.3720.5760.104
recency0.001-0.4151.000-0.502-0.408-0.4350.0470.1080.019-0.120-0.0980.015
qtde_invoices0.0250.770-0.5021.0000.7170.6900.059-0.2580.0780.2930.101-0.181
qtde_items-0.0700.927-0.4080.7171.0000.7310.168-0.2270.0800.3450.7290.147
qtde_products0.0120.744-0.4350.6900.7311.000-0.377-0.1660.0360.2430.3840.516
avg_ticket-0.1300.2460.0470.0590.168-0.3771.000-0.1220.0910.1890.189-0.618
avg_recency_days0.019-0.2480.108-0.258-0.227-0.166-0.1221.000-0.881-0.396-0.0770.130
frequency-0.0020.0900.0190.0780.0800.0360.091-0.8811.0000.2340.027-0.121
qtdade_itens_retornados-0.0630.372-0.1200.2930.3450.2430.189-0.3960.2341.0000.212-0.053
avg_basket_size-0.1230.576-0.0980.1010.7290.3840.189-0.0770.0270.2121.0000.403
avg_unique_basket_size-0.0170.1040.015-0.1810.1470.516-0.6180.130-0.121-0.0530.4031.000

Missing values

2023-04-29T23:54:06.232181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-29T23:54:06.388141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idmonetaryrecencyqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtdade_itens_retornadosavg_basket_sizeavg_unique_basket_size
0178505391.21372.0034.001733.00297.0018.1535.5017.0040.0050.970.62
1130473232.5956.009.001390.00171.0018.9027.250.0335.00154.4411.67
2125836705.382.0015.005028.00232.0028.9023.190.0450.00335.207.60
313748948.2595.005.00439.0028.0033.8792.670.020.0087.804.80
415100876.00333.003.0080.003.00292.008.600.0722.0026.670.33
5152914623.3025.0014.002102.00102.0045.3323.200.0429.00150.144.36
6146885630.877.0021.003621.00327.0017.2218.300.06399.00172.437.05
7178095411.9116.0012.002057.0061.0088.7235.700.0341.00171.423.83
81531160767.900.0091.0038194.002379.0025.544.140.24474.00419.716.23
9160982005.6387.007.00613.0067.0029.9347.670.020.0087.574.86
customer_idmonetaryrecencyqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtdade_itens_retornadosavg_basket_sizeavg_unique_basket_size
5626177271060.2515.001.00645.0066.0016.066.001.006.00645.0066.00
563617232421.522.002.00203.0036.0011.7112.000.150.00101.5015.00
563717468137.0010.002.00116.005.0027.404.000.400.0058.002.50
564813596697.045.002.00406.00166.004.207.000.250.00203.0066.50
5654148931237.859.002.00799.0073.0016.962.000.670.00399.5036.00
565812479473.2011.001.00382.0030.0015.774.001.0034.00382.0030.00
567914126706.137.003.00508.0015.0047.083.000.7550.00169.334.67
5685135211092.391.003.00733.00435.002.514.500.300.00244.33104.00
569515060301.848.004.00262.00120.002.521.002.000.0065.5020.00
571412558269.967.001.00196.0011.0024.546.001.00196.00196.0011.00